MRI-based computational hemodynamics in patients with aortic coarctation using the lattice Boltzmann methods: Clinical validation study

Abstract

Purpose: To introduce a scheme based on a recent technique in computational hemodynamics, known as the lattice Boltzmann methods (LBM), to noninvasively measure pressure gradients in patients with a coarctation of the aorta (CoA). To provide evidence on the accuracy of the proposed scheme, the computed pressure drop values are compared against those obtained using the reference standard method of catheterization.

Materials and methods: Pre- and posttreatment LBM-based pressure gradients for 12 patients with CoA were simulated for the time point of peak systole using the open source library OpenLB. Four-dimensional (4D) flow-sensitive phase-contrast MRI at 1.5 Tesla was used to acquire flow and to setup the simulation. The vascular geometry was reconstructed using 3D whole-heart MRI. Patients underwent pre- and postinterventional pressure catheterization as a reference standard.

Results: There is a significant linear correlation between the pretreatment catheter pressure drops and those computed based on the LBM simulation, r=.85, P<.001. The bias was -0.58 ± 4.1 mmHg and was not significant ( P=0.64) with a 95% confidence interval (CI) of -3.22 to 2.06. For the posttreatment results, the bias was larger and at -2.54 ± 3.53 mmHg with a 95% CI of -0.17 to -4.91 mmHg.

Conclusion: The results indicate a reasonable agreement between the simulation results and the catheter measurements. LBM-based computational hemodynamics can be considered as an alternative to more traditional computational fluid dynamics schemes for noninvasive pressure calculations and can assist in diagnosis and therapy planning.

Level of evidence: 3 J. Magn. Reson. Imaging 2017;45:139-146.

Keywords: aortic coarctation; catheterization; computational fluid dynamics; lattice Boltzmann method; magnetic resonance imaging; pressure gradient.

Figure 1

a: Pretreatment coarctation geometries. b: Posttreatment geometries. Case 4 was not treated. Treatment was performed through a stenting procedure to expand the narrowed region in the aortic arch.

Figure 2

Extraction of flow rate curves at the inlet (red contour) and outlet (green contour) of the aorta geometry for simulation setup. a: The 3D WH MRI (anatomy). b: PC MR angiography (PC MRA). c: Anatomy after alignment with PC MRA through registration. d: The segmented geometry. e,f: Represent the magnitude of the velocity. g: Red: inlet flowrate curve. Green: outlet flowrate curve. The flow is distributed into the branches by subtracting the inlet and outlet flow rate at the time point of peak systole (red vertical line).

Figure 3

Lattice configuration D3Q19 in three dimensions with 19 discrete velocity directions.

Figure 4

Lattice Boltzmann voxelization for a patient‐specific aorta geometry. a: Blocks are for parallelization and are distributed between processors. Each block normally contains between 1000 to 10,000 grid cells. b: The boundaries are fitted by taking the exact distances along the 19 paths from each voxel which is inside the computational domain and close to the wall. c: Streamline visualization of the simulated velocity.

Figure 5

Bland‐Altman plots demonstrating the agreement between pressure gradients measured by catheter and LBM. a: pretreatment. b: posttreatment. Reference lines are mean and ± 1.96 × SD.